Diabetes and the Quantified Self

Today I went to a talk by an academic colleague about the Quantified Self movement — that is, a community of people who use technology to track their health. The speaker was focusing in particular on technologies that tell women when they’re likely to be fertile by tracking their body temperature each day. But that’s far from the only thing that Quantified Self-ers aim to analyze. Blood pressure, weight, food intake, heart rate, sleep patterns, you name it — as sensors improve and apps explode, it’s becoming increasingly possible to see yourself not as a living, breathing human, but as a series of charts and graphs and numerical values. As I listened to her describe people’s attempts to turn their bodies into logical, quantifiable systems of inputs and outputs, I felt a pang of recognition: she was describing what it’s like to live with diabetes.

I first became aware of the term “self-quantification” (and, for that matter, the Quantified Self) not through diabetes per se, but through an article I did for O Magazine for which I deliberately overdosed on health-tracking technology. I wore a Zeo sleep monitor; I strapped on a Digifit; I monitored my heart rate and my water intake; I started each morning by hopping onto a Withings wireless-enabled scale. It was an interesting experiment, but eventually I got tired of it. It was exhausting to monitor every aspect of my health.

The irony, of course, is that thanks to diabetes, I’m already living the tagline of the Quantified Self: “self-knowledge through numbers.” I’m always thinking about what I eat. I’m constantly tracking my blood sugar via my Dexcom CGM, and crunching the numbers from my finger sticks and food to figure out how much insulin to give myself. Living with a busted pancreas has made self-tracking a mandatory part of my life.

Sometimes the effect of this constant quantification is negative: like many people with diabetes, I judge myself by the numbers on my glucometer’s screen and become angry or feel like a failure when the number is not what I want. Doctors are always asking for food, exercise and medication logs so they can decide whether we’ve been “good” or “bad.” Nutritionists get pissed at us when the input we get from our food doesn’t result in a perfect blood sugar output. And, of course, all of this self-tracking is exhausting.

At the same time, numbers – and the ability to quantify them – are essential tools in diabetes management. I certainly wouldn’t want to go back to the days where the only way to measure your glucose was via colors on a urine stick. And while there are times when I want to throw my Dexcom G4 Platinum CGM across the room, I am deeply grateful for the information and reassurance it provides. I actually look forward to the day when diabetes becomes even more quantifiable – when apps and (Mac-compatible) software exists that will wirelessly pull data from all of my devices and display them on one integrated screen, so that I can take the thousands of data points I collect each week and use them to find patterns. (Today’s diabetes management software is woefully inadequate.) I want software that will translate the language of my body into numbers I can use to improve my self-care.

In today’s talk, my colleague was hypothesizing that the general fad of technological health tracking represents a desire to de-humanize ourselves, to disassociate from our bodies and become app-dependent cyborgs. Like many people in the audience, she was suspicious of the tracking trend, particularly when it involved what she called the “black box” — situations where the computer or app spits out a judgment without any explanation of how the interpretation came to be. The fertility app she was describing was a great example of this “black box” concept: it took the woman’s temperature, compared it to her past readings, and displayed a red or green signal indicating whether or not she was fertile– essentially taking the interpretation out of the woman’s hands and putting it into those of the software developers.

The parallels with diabetes seemed obvious, both in good and bad ways. On what I consider the negative side, there’s the hemoglobin A1c. It’s an essential screening tool for diabetes, that’s for sure, and it does provide information that finger sticks can’t. I do not want to get rid of it. But at the same time, it’s a black box whose actual meaning is surprisingly undefined. As I learned at the ADA conference this summer, there’s no agreement on what average blood glucose levels the A1c’s percentages actually correspond to. What’s more, there are variations between individual people, so that your A1c and my A1c could differ, even if our average blood glucose values were exactly the same. And yet we and our doctors judge our “performance” as diabetics based on that number, taking it as an undisputed fact.

On the flip side, a black box is exactly what my pancreas used to be. What are our bodies, after all, but mysteries made of flesh? Would you really want to be responsible for tracking and interpreting your heart rate? Diabetes has made me appreciate all the things my body does, silently and miraculously, all day. I cannot wait for there to be a black box for diabetes – by which I mean an artificial pancreas – which will take the interpretation and numbers out of my hands and relegate it to a machine whose algorithms I have no desire to understand. To me, that would be second only to a cure.

Comments (4)

Thank you so much for post. I would like to get more information about the talk you saw. I wonder if the academic colleague you mentioned has talking directly with anybody in the Quantified Self community, or is working mainly from second hand descriptions. This is not to disparage the analysis, which of course I myself am only encountering second hand. In fact, the question of “the black box” is very important. But it is one that is very actively discussed among people who self-identify as being involved in Quantified Self practices, come to events, and share their knowledge. I think of Quantified Self as part of the answer to dehumanizing elements in black box tracking, as we base our work on direct conversation, often in-person, that answers three questions: “What did you do? How did you do it? What did you learn?” By putting learning in the center of our inquiry, we try to replace the black box with articulate self-reflection. There are many academic participants in our meetings, and I hope your colleague will join one and bring his or her analysis into the conversation.

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